Should I Centralize My Data in Amazon Redshift?

Amazon Redshift remains a strong AWS-native option for structured business intelligence, predictable reporting, and low-latency analytics, but centralizing all organizational data in it can introduce concurrency constraints, tuning and maintenance work, proprietary storage lock-in, data movement costs, and fragile ETL pipelines. The piece presents open lakehouse architectures based on Amazon S3 and Apache Iceberg as an alternative that keeps data in open formats while providing warehouse-like capabilities such as transactions, schema evolution, and time travel. It positions Starburst, built on Trino, as a federated query layer that can access and join data across S3, databases, cloud platforms, and SaaS sources without copying it into a central warehouse, while noting that its performance and cost comparisons with Redshift are vendor-stated and unverified. Rather than recommending a wholesale replacement, it advocates a hybrid approach in which Redshift supports refined, high-performance BI workloads and a lakehouse plus federation supports large-scale, semi-structured, cross-source, AI, and exploratory analytics.

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